The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Magnetic Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of the large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2D”) image made of pixel elements or a three-dimensional (“3D”) image made of volume elements (“voxels”). Such 2D or 3D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Two fundamental questions frequently asked during medical image interpretation are: (1) “Where is the anatomical structure?” and (2) “What is the orientation of the anatomical structure?” “Anatomy orientation” generally refers to one or more intrinsic directions of an anatomical structure. These directions may be defined by local landmarks or global organ shapes, and are consistent across the population.
Early studies of anatomy orientation detection often exploit prior knowledge of specific anatomies. For example, template matching that relies on the Matched Filter Theorem or eigenanalysis of Hessian matrix has been widely used to determine local vessel orientation and perform tracing. These methods are dependent on strong priors of the anatomy under study, and do not generalize to other anatomies, such as the heart or the vertebral-column.
From another perspective, machine learning technologies have revolutionized the landscape of medical image analysis. In the areas of anatomy detection or segmentation, learning-based approaches have provided a generic solution for different organs in different modalities. However, learning technologies in detecting anatomy orientation remain under-explored.